Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data

Transportation Research Part A: Policy and Practice

Volumn 61, Issue , 2014, Pages 27-40

  Paefgen, Johannes ^a   Staake, Thorsten ^b   Fleisch, Elgar ^a,c  

^a UNIVERSITY OF ST GALLEN   (Switzerland)

^b UNIVERSITY OF BAMBERG   (Germany)

^c ETH ZURICH   (Switzerland)

Author keywords

Accident research; GPS trajectories; In vehicle data recorder; Multivariate logistic regression; Pay as you drive insurance; Risk exposure

Indexed keywords

COMMERCIAL VEHICLES; DIGITAL STORAGE; LOGISTIC REGRESSION; RISK MANAGEMENT;

ACCIDENT RESEARCH; DATA RECORDERS; GPS TRAJECTORIES; MULTIVARIATE LOGISTIC REGRESSIONS; RISK EXPOSURE;

ACCIDENTS;

ACCIDENT PREVENTION; CONCEPTUAL FRAMEWORK; DATA SET; GPS; INSURANCE SYSTEM; MULTIVARIATE ANALYSIS; REGRESSION ANALYSIS; RISK ASSESSMENT;

EID: 84892664317     PISSN: 09658564     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tra.2013.11.010     Document Type: Article

References (54)

1. 2nd Strategic Highway Research Program, 2012. Naturalistic Driving Study. (accessed 23.11.12). http://www.shrp2nds.us/.
2. Akaike H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 1974, AC-19(6):716-723.
3. Biding, T., Lind, G., 2002. Intelligent Speed Adaptation, Results of Large-scale Trials in Borlänge, Lidköping, Lund and Umea during the Period 1999 to 2002. Technical Report. Swedish National Road Administration.
4. Blanchard R., Myers A.M., Porter M.M. Correspondence between self-reported and objective measures of driving exposure and patterns in older drivers. Accid. Anal. Prev. 2010, 42(2):523-529.
5. Bolderdijk J.W., Knockaert J., Steg E.M., Verhoef E.T. Effects of pay-as-you-drive vehicle insurance on young drivers' speed choice: results of a Dutch field experiment. Accid. Anal. Prev. 2011, 43(3):1181-1186.
6. Bordoff, J.E., Noel, P.J., 2008. Pay-as-you-drive Auto Insurance: A Simple Way to Reduce Driving-related Harms and Increase Equity. The Brookings Institution. http://www.brookings.edu/papers/2008/07_payd_bordoffnoel.aspx.
7. Boyce T.E., Geller E.S. An instrumented vehicle assessment of problem behavior and driving style: do younger males really take more risks. Accid. Anal. Prev. 2002, 34(1):51-64.
8. Chipman M. The role of exposure, experience and demerit point levels in the risk of collision. Accid. Anal. Prev. 1982, 14(6):475-483.
9. Chipman M.L., MacGregor C.G., Smiley A.M., Lee-Gosselin M. Time vs. distance as measures of exposure in driving surveys. Accid. Anal. Prev. 1992, 24(6):679-684.
10. Chipman M.L., MacGregor C.G., Smiley A.M., Lee-Gosselin M. The role of exposure in comparisons of crash risk among different drivers and driving environments. Accid. Anal. Prev. 1993, 25(2):207-211.
11. Christensen R. Log-linear Models and Logistic Regression 1997, Springer, New York. second ed.
12. Cox D.R., Snell E.J. A general definition of residuals. J. Roy. Stat. Soc. Ser. B Stat. Methodol. 1968, 30(2):248-275.
13. Desyllas P., Sako M. Profiting from business model innovation: evidence from Pay-As-You-Drive auto insurance. Res. Policy 2012, 42(1):101-116.
14. Ferreira, J., Minike, E., 2010. Pay-as-you-drive Auto Insurance in Massachusetts: A Risk Assessment and Report on Consumer, Industry and Environmental Benefits. Department of Urban Studies and Planning, Massachusetts Institute of Technology. http://www.clf.org/our-work/healthy-communities/modernizing-transportation/pay-as-you-drive-auto-insurance-payd.
15. Foldvary L. Road accident involvement per miles travelled, Part II. Accid. Anal. Prev. 1975, 11(2):191-205.
16. Forrest T.L., Pearson D.F. Comparison of trip determination methods in household travel surveys enhanced by a global positioning system. Transp. Res. Rec. 2005, 1917:63-71.
17. Gordon T.J., Kostyniuk L.P., Green P.E., Barnes M.A., Blower D., Blankespoor A.D., Bogard S.E. Analysis of crash rates and surrogate events. Transp. Res. Rec. 2011, 2237:1-9.
18. Helander M., Hagvall B. An instrumented vehicle for studies of driver behavior. Accid. Anal. Prev. 1976, 8:271-277.
19. Hjälmdahl M., Varhelyi A. Validation of in-car observations, a method for driver assessment. Transp. Res. Part A Policy Pract. 2004, 38(2):127-142.
20. Huang, H., Zhu, Y., Li, X., Li, M., Wu, M.-Y., 2010. META: a mobility model of MEtropolitan TAxis extracted from GPS traces. In: Proceedings of the 2010 IEEE Wireless Communication and Networking Conference.
21. ISTAT, 2009. Incidenti Stradali. Report of the Italian Institute of Statistics.
22. Janke M.K. Accidents, mileage, and the exaggeration of risk. Accid. Anal. Prev. 1991, 23(2-3):183-188.
23. Jolliffe I. Principal component analysis. Encyclopedia of Statistics in Behavioral Science 2005, John Wiley & Sons, Ltd., Hoboken, NJ.
24. Jovanis P.P., Chang H.L. Modeling the relationship of accidents to miles traveled. Transp. Res. Rec. 1986, 1068:42-51.
25. Jovanis P.P., Aguero-Valverde J., Wu K.-F., Shankar V. Analysis of naturalistic driving event data. Transp. Res. Rec. 2011, 2236:49-57.
26. Jun J., Ogle J., Guensler R. Relationships between crash involvement and temporal-spatial driving behavior activity patterns: use of data for vehicles with global positioning systems. Transp. Res. Rec. 2007, 2019:246-255.
27. Jun J., Guensler R., Ogle J. Differences in observed speed patterns between crash-involved and crash-not-involved drivers: application of in-vehicle monitoring technology. Transp. Res. Part C Emerg. Technol. 2010, 19(4):569-578.
28. Langford J., Koppel S., McCarthy D., Srinivasan S. In defence of the "low-mileage bias". Accid. Anal. Prev. 2008, 40(6):1996-1999.
29. Lemeshow S., Hosmer D.W. A review of goodness of fit statistics for use in the development of logistic regression models. Am. J. Epidemiol. 1982, 115(1):92-106.
30. Litman, T., 2011. Distance-based Vehicle Insurance Feasibility, Benefits and Costs: Comprehensive Technical Report. VTPI. http://www.vtpi.org/dbvi_com.pdf.
31. Lord D., Mannering F. The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transp. Res. Part A Policy Pract. 2010, 44(5):291-305.
32. Lourens P.F., Vissers J.A., Jessurun M. Annual mileage, driving violations, and accident involvement in relation to drivers' sex, age, and level of education. Accid. Anal. Prev. 1999, 31:593-597.
33. Mayou R., Bryant B., Duthie R. Psychiatric consequences of road traffic accidents. Br. Med. J. 1993, 307(6905):647.
34. Musicant Oren, Bar-gera H., Schechtman E. Electronic records of undesirable driving events. Transp. Res. Part F Traffic Psychol. Behav. 2010, 13(2):71-79.
35. Nagelkerke N.J.D. A note on a general definition of the coefficient of determination. Biometrika 1991, 78(3):691-692.
36. Ogle J., Guensler R., Elango V. Georgia's Commute Atlanta value pricing program: recruitment methods and travel diary response rates. Transp. Res. Rec. 2005, 1931:28-37.
37. Peduzzi P., Concato J., Kemper E., Holford T.R., Feinstein A.R. A simulation study of the number of events per variable in logistic regression analysis. Clin. Epidemiol. 1996, 49(12):1373-1379.
38. Progressive Insurance, 2005. Texas Mileage Study: Relationship between Annual Mileage and Insurance Losses. Report.
39. Risk A., Shaoul J. Exposure to risk and the risk of exposure. Accid. Anal. Prev. 1982, 14(5):353-357.
40. Schoenfeld D.A., Borenstein M. Calculating the power or sample size for the logistic and proportional hazards models. J. Stat. Comput. Simul. 2005, 75(10):771-785.
41. Schulz K.F., Grimes D.A. Case-control studies: research in reverse. Lancet Epidemiol. Ser. 2002, 359:431-434.
42. Schwarz G. Estimating the dimension of a model. Ann. Stat. 1978, 6(2):461-464.
43. Shankar V., Jovanis P.P., Aguero-Valverde J., Gross F. Analysis of naturalistic driving data: prospective view on methodological paradigms. Transp. Res. Rec. 2008, 2061:1-8.
44. Staplin L., Gish K.W., Joyce J. "Low mileage bias" and related policy implications - a cautionary note. Accid. Anal. Prev. 2008, 40(3):1249-1252.
45. Stopher P., Fitzgerald C., Xu M. Assessing the accuracy of the Sydney Household Travel Survey with GPS. Transportation 2007, 34:723-741.
46. Toledo T., Musicant O., Lotan T. In-vehicle data recorders for monitoring and feedback on drivers' behavior. Transp. Res. Part C Emerg. Technol. 2008, 16(3):320-331.
47. Wahlberg A.af. The stability of driver acceleration behavior, and a replication of its relation to bus accidents. Accid. Anal. Prev. 2004, 36(1):83-92.
48. White S.B. On the use of annual vehicle miles of travel estimates from vehicle owners. Accid. Anal. Prev. 1976, 8(4):257-261.
49. Wolf J., Guensler R., Bachman W. Elimination of the travel diary experiment to derive trip purpose from Global Positioning System travel data. Transp. Res. Rec. 2001, 1768:125-134.
50. Wolf J., Oliveira M., Thompson M. Impact of underreporting on mileage and travel time estimates: results from Global Positioning System-enhanced household travel survey. Transp. Res. Rec. 2003, 1854:189-198.
51. Wolfe A.C. The concept of exposure to the risk of a road traffic accident and an overview of exposure data collection methods. Accid. Anal. Prev. 1982, 14(5):337-340.
52. Wouters I.J., Bos J.M. Traffic accident reduction by monitoring driver behaviour with in-car data recorders. Accid. Anal. Prev. 2000, 32(5):643-650.
53. Wu K.-F., Jovanis P.P. Crashes and crash-surrogate events: exploratory modeling with naturalistic driving data. Accid. Anal. Prev. 2012, 45:507-516.
54. Zhang D., Li N., Zhou Z.-H., Chen C., Sun L., Li S. IBAT: detecting anomalous taxi trajectories from GPS traces. Ubiquitous Computing 2011, 99-108. ACM, Beijing, China.